Personalized news recommendation is highly time-sensitive, as user interests are often driven by emerging events, trending topics, and shifting real-world contexts. These dynamics make it essential to model not only users' long-term preferences, which reflect stable reading habits and high-order collaborative patterns, but also their short-term, context-dependent interests that change rapidly over time. However, most existing approaches rely on a single static interaction graph, which struggles to capture both long-term preference patterns and short-term interest changes as user behavior evolves. To address this challenge, we propose a unified framework that learns user preferences from both global and local temporal perspectives. A global preference modeling component captures long-term collaborative signals from the overall interaction graph, while a local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics. Within this module, an LSTM branch models the progressive evolution of recent interests, and a self-attention branch captures long-range temporal dependencies. Extensive experiments on two large-scale real-world datasets show that our approach consistently outperforms strong baselines and delivers fresher and more relevant recommendations across diverse user behaviors and temporal settings.

翻译：个性化新闻推荐具有高度时效性，因为用户兴趣常受新兴事件、热门话题及不断变化的现实情境驱动。这些动态特性使得建模不仅需要关注反映稳定阅读习惯和高阶协同模式的用户长期偏好，还需捕捉随时间快速变化的短期情境依赖性兴趣。然而，现有方法大多依赖单一的静态交互图，难以在用户行为演化过程中同时捕获长期偏好模式与短期兴趣变化。为应对这一挑战，我们提出一个从全局与局部时间视角共同学习用户偏好的统一框架。全局偏好建模组件从整体交互图中捕获长期协同信号，而局部偏好建模组件则将历史交互划分为阶段性时序子图以表征短期动态。在该模块中，LSTM分支建模近期兴趣的渐进演化过程，自注意力分支则捕获长程时间依赖关系。基于两个大规模真实数据集的广泛实验表明，本方法在多种用户行为与时间场景下均持续优于现有基线模型，并能提供更新颖、更相关的推荐结果。